Sexual Misconduct: What Does a 20-Year Review of Cases in Quebec Reveal about the Characteristics of Professionals, Victims, and the Disciplinary Process?
Bibliographic record
Abstract
While there is a considerable body of literature on sexual aggression, we know much less about the violation of sexual boundaries within professional relationships. To address this knowledge gap, the characteristics of cases of sexual misconduct in the province of Quebec were extracted, based on a search of published disciplinary decisions between 1998 and 2020, using the legal databases CANLII and SOQUIJ. The search yielded 296 decisions including 249 male and 47 female members from 22 professional orders, and involving 470 victims. Results indicate that male professionals approaching mid-career accounted for a greater proportion of cases of sexual misconduct. Moreover, physical and mental health professionals were overrepresented in cases, as were female adult victims. Acts of sexual misconduct concerned mostly sexual touching and intercourse and occurred during consultations. Female professionals were more inclined to establish romantic and sexual relationships with clients than their male counterparts. Of the 92.0% of professionals found guilty of at least one count of sexual misconduct, two thirds eventually returned to practice. Following the guilty verdict, few faced rehabilitative measures. Recommendations are provided for the prevention of sexual recidivism and the accompaniment of victims of sexual misconduct throughout the disciplinary process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".